Tagging sentiment

This tutorials shows you how to do sentiment analysis in Flair.

Tagging sentiment with our standard model

Our standard sentiment analysis model uses distilBERT embeddings and was trained over a mix of corpora, notably the Amazon review corpus, and can thus handle a variety of domains and language.

Let’s use an example sentence:

from flair.nn import Classifier
from flair.data import Sentence

# load the model
tagger = Classifier.load('sentiment')

# make a sentence
sentence = Sentence('This movie is not at all bad.')

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence)

This should print:

Sentence[8]: "This movie is not at all bad." → POSITIVE (0.9929)

Showing us that the sentence overall is tagged to be of POSITIVE sentiment.

Tagging sentiment with our fast model

We also offer an RNN-based variant which is faster but less accurate. Use it like this:

from flair.nn import Classifier
from flair.data import Sentence

# load the model
tagger = Classifier.load('sentiment-fast')

# make a sentence
sentence = Sentence('This movie is very bad.')

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence)

This should print:

Sentence[6]: "This movie is very bad." → NEGATIVE (0.9999)

This indicates that the sentence is of NEGATIVE sentiment. As you can see, its the same code as above, just loading the ‘sentiment-fast’ model instead of ‘sentiment’.

List of Sentiment Models

We end this section with a list of all models we currently ship with Flair:

ID	Language	Task	Training Dataset	Accuracy
‘sentiment’	English	detecting positive and negative sentiment (transformer-based)	movie and product reviews	98.87
‘sentiment-fast’	English	detecting positive and negative sentiment (RNN-based)	movie and product reviews	96.83
‘de-offensive-language’	German	detecting offensive language	GermEval 2018 Task 1	75.71 (Macro F1)